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clarity-ai/

Clarity AI Flux Upscaler sharpens images while preserving natural textures and edges, with prompt-guided refinement and LoRA support. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

upscaler
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$0.2cho mỗi lần chạy·~50 / $10

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README

Clarity AI Flux Upscaler

Clarity AI Flux Upscaler enhances and enlarges images with prompt-guided refinement, optional LoRA style control, and adjustable creativity. It is designed for high-quality super-resolution workflows where you want both sharper detail and more control over the final look.

Why Choose This?

  • Prompt-guided upscaling Use a text prompt to steer texture, tone, lighting, or style during enhancement.

  • Optional LoRA control Add a compatible lora_link when you want stronger domain-specific styling.

  • Megapixel-based output sizing Choose the target output size directly in megapixels for predictable delivery.

  • Flexible enhancement strength Adjust creativity to balance faithful restoration against more generative detail.

  • Production-ready workflow Suitable for portraits, fashion, product imagery, artwork, and other high-resolution creative assets.

Parameters

ParameterRequiredDescription
imageYesInput image to upscale.
target_megapixelsNoTarget output size in megapixels. Higher values produce larger and more detailed outputs.
promptNoOptional prompt to guide tone, texture, lighting, or visual refinement.
lora_linkNoOptional URL to a compatible LoRA for additional style control.
creativityNoControls how much new detail is added. Lower values stay closer to the source, while higher values add stronger enhancement.

How to Use

  1. Upload your image — provide the source image you want to enhance.
  2. Choose target megapixels — set the desired output size based on your delivery needs.
  3. Add a prompt (optional) — describe the look, texture, or mood you want.
  4. Add a LoRA link (optional) — provide lora_link if you want extra style steering.
  5. Adjust creativity (optional) — keep it low for more faithful results, or raise it for stronger generated detail.
  6. Submit — run the model and download the enhanced image.

Example Prompt

Elegant editorial texture, soft natural light, refined fabric detail, realistic skin tones, premium fashion photography look

Pricing

Pricing is based on the selected target_megapixels tier.

Target MegapixelsCost
<= 4 MP$0.20
> 4 MP and <= 8 MP$0.40
> 8 MP and <= 16 MP$0.60
> 16 MP and <= 25 MP$1.20
> 25 MP and <= 50 MP$2.40
> 50 MP$3.20

Billing Rules

  • Pricing is based on target_megapixels
  • Cost uses fixed megapixel tiers rather than scaling linearly
  • Moving into a higher megapixel tier increases the price to the next bracket
  • prompt, lora_link, and creativity do not affect pricing

Best Use Cases

  • Fashion and portrait enhancement — Improve detail while preserving skin tones, fabrics, and visual polish.
  • Product image refinement — Generate sharper commercial assets for catalogs, campaigns, and listings.
  • Artwork and illustration upscaling — Create larger outputs with more controlled stylization.
  • Prompt-guided image finishing — Use short prompts to nudge mood, texture, and overall visual tone.
  • Style-directed super-resolution — Combine megapixel scaling with LoRA guidance for custom looks.
  • Premium asset preparation — Produce high-resolution images for design, publishing, and creative delivery.

Pro Tips

  • Start with a lower megapixel tier first, then increase only if you need a larger final output.
  • Keep creativity lower when identity, structure, and source fidelity matter most.
  • Use short, specific prompts instead of long descriptive paragraphs.
  • Add lora_link only when you need a stronger style direction.
  • Use the cleanest source image available for better detail recovery.
  • For fashion, portrait, or editorial work, subtle prompts usually produce more controllable results.

Notes

  • image is the only required field.
  • Pricing depends on the selected target_megapixels tier.
  • prompt, lora_link, and creativity change the look of the result, but not the price.
  • Larger target sizes are better suited for print, premium delivery, and high-resolution commercial use.

Related Models

Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp. Giá trong tài liệu chỉ để tham khảo và có thể đã lỗi thời. Nút Generate hiển thị giá ước tính; phí cuối cùng của tác vụ sẽ được áp dụng.

Flux Upscaler API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/clarity-ai/flux-upscaler with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Flux Upscaler below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "target_megapixels": 4,
    "creativity": 0
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/clarity-ai/flux-upscaler" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/clarity-ai/flux-upscaler";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "target_megapixels": 4,
        "creativity": 0
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "target_megapixels": 4,
    "creativity": 0
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/clarity-ai/flux-upscaler", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Flux Upscaler API — Frequently asked questions

What is the Flux Upscaler API?

Flux Upscaler is a Clarity model for upscaling, exposed as a REST API on WaveSpeedAI. Clarity AI Flux Upscaler sharpens images while preserving natural textures and edges, with prompt-guided refinement and LoRA support. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Flux Upscaler API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/clarity-ai/clarity-ai-flux-upscaler.

How much does Flux Upscaler cost per run?

Flux Upscaler starts at $0.20 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Flux Upscaler accept?

Key inputs: `image`, `creativity`, `target_megapixels`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/clarity-ai/clarity-ai-flux-upscaler.

How long does Flux Upscaler take to generate?

Median end-to-end generation time on WaveSpeedAI is around 106 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Flux Upscaler outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Clarity). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Flux Upscaler | AI Image Upscaler API on WaveSpeedAI